Chris J. Maddison

Assistant Professor
Dept. of Computer Science and Dept. of Statistical Sciences
University of Toronto

CIFAR AI Chair, Vector Institute
Research Scientist, DeepMind

cmaddis [at] cs [dot] [city] [dot] edu

Google Scholar, Biography


I work on machine learning, which is the study of algorithms that can learn to solve problems from examples.

Large models and transfer learning      Large language models like ChatGPT demonstrate that training large models on many inter-related tasks can have a synergistic effect. I am interested in understanding and applying these principles to improve machine learning in data-constrained settings.

Learning and optimization      Algorithms for statistical inference and optimization are the engines that drive machine learning. Although inference and optimization may seem like distinct problems, there is a close interplay between them. I am interested in this interplay.

Applications      You can improve machine learning algorithms when you know something about the application domain. I have long been interested in applications that involve discrete reasoning, for example when we built the first artificial agent that plays the board game Go at a superhuman level. I am now interested in applying these principles to biochemistry.

Selected Publications

Please check my Google Scholar for a complete list of my publications.


Prospective members      If you would like to study for a graduate degree with me, you should apply through the CS department or the Statistics department. If you would like to work with me as a postdoctoral researcher, I encourage you to apply through the Vector Institute.

Current members

Previous members



Here are some of my recorded talks, which cover the spectrum from academic talks to wistful reflections.